Mobile communications device with electronic nose

ABSTRACT

Systems and methods for a mobile electronic system that gathers and analyzes odors, airborne chemicals and/or compounds. A signature or representation of the odors, airborne chemicals and/or compounds can be generated. Extrinsic data associated with the odors, airborne chemicals and/or compounds or capturing the odors, airborne chemicals and/or compounds can be identified. A model can be generated based on the representation and the extrinsic data. Filters can be generated based on the extrinsic data. The model can be searched for candidate matches, solutions, or other results based on the representation and the filters. Results can be generated based on the search and candidate matches.

PRIORITY CLAIM

This patent application is a continuation-in-part of U.S. patentapplication Ser. No. 14/560,779, filed on Dec. 4, 2014, entitled MOBILECOMMUNICATIONS DEVICE WITH ELECTRONIC NOSE, which is a continuation ofU.S. patent application Ser. No. 14/219,914, filed on Mar. 19, 2014,entitled MOBILE COMMUNICATIONS DEVICE WITH ELECTRONIC NOSE, which is acontinuation-in-part (CIP) of U.S. patent application Ser. No.13/839,206, filed on Mar. 15, 2013, entitled ELECTRONIC NOSE SYSTEM ANDMETHOD, which claims the benefit of priority to U.S. Provisional PatentApplication Ser. No. 61/643,781, filed on May 7, 2012, entitledELECTRONIC NOSE SYSTEM AND METHOD.

These patent applications are respectively incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates to detection of chemical compounds, gases, andodor. More particularly to methods and systems for detection ofchemicals or gases in air samples through a portable handheld device.

BACKGROUND

The proliferation, advancement, and affordability of electroniccomputing devices such as smart phones, laptop computers, personalcomputers, digital cameras, tablets, personal digital assistants (PDAs)and other electronic devices has made powerful electronic devices moreavailable to the general public than ever before. Advancements indetection devices capable of odor detection, chemical detection and gasdetection have made some detection devices common place in homes. Forexample, a sensor that can indicate presence of a chemical, gas orsubstance of interest can be useful to identify an unacceptable level ofa toxic or explosive gas. There is an unmet need by the state of the artfor convenient, rapid and reliable identification or detection ofchemicals, gases, compounds, substances and the like.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification nor delineate the scope of any particularimplementations of the specification, or any scope of the claims. Itspurpose is to present some concepts of the specification in a simplifiedform as a prelude to the more detailed description that is presented inthis disclosure.

Systems and methods disclosed herein relate to detection of odors,chemicals and gasses via handheld electronic devices (e.g., mobilephones). A sample delivery component is coupled to an electronicprocessor. The sample delivery component collects a headspace of asample. The headspace is a portion of the sample that is to be analyzed.The sample delivery component can passively and/or actively collect theheadspace of a sample by drawing air, for example.

A detection component is coupled to the electronic processor and sampledelivery component. The detection component can analyze the headspace.The headspace analysis can determine presence and ratio of chemical,physical, and/or visual substances the make-up the headspace. Aspects ofthe detection component and the electronic processor can be coupled to acomputer readable memory. The memory can store known analyzed samples ofchemicals, gases, and/or odors, e.g., in the form of digital signatures,hash values, or any suitable use of identifying indicia orrepresentation. The detection component can compare the analyzedheadspace to known analyzed samples in the memory to determine thesource of the headspace (e.g., flower, foodstuff, alcohol, perfume,etc.) and/or associate the analyzed headspace with a known source. Inanother example, when an analyzed headspace is determined to be a newcombination of odors, gases, and/or chemicals, then the new combinationof odors, gases, and/or chemicals can be stored in the memory.

In another embodiment, the detection component can determine if theheadspace is a visual gas such as smoke without comparing the headspaceto samples stored in memory. In this embodiment, the detection componentcan visually analyze the headspace.

In another embodiment, an image detection component (e.g., camera) cancapture an image of a source of the headspace. The detection componentcan receive the captured image and determine the source of the headspacevia analysis of the captured image, the analyzed headspace, or acombination thereof.

A display component displays can display a result of the analyzedheadspace. The result can be a known source (e.g., type of flower, typeof perfume, etc.). The result can comprise text and/or image. In oneexample, a result can be saved in memory and associated with as a newodor, gas, or chemical source.

The following description and the drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the following detaileddescription of the specification when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, implementations, and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 illustrates a high-level functional block diagram of an examplesystem comprising a mobile electronic nose device in accordance withvarious aspects disclosed herein;

FIG. 2 illustrates a high-level functional block diagram of an examplesystem comprising a mobile electronic nose device including an inputcomponent and an output component in accordance with various aspectsdisclosed herein;

FIG. 3 illustrates a high-level functional block diagram of an examplesystem comprising a mobile electronic nose device in communication witha server in accordance with various aspects disclosed herein;

FIG. 4 illustrates a high-level functional block diagram of an examplesystem comprising a sample delivery component in accordance with variousaspects disclosed herein;

FIG. 5 illustrates an example schematic diagram of a system comprising asample delivery component in accordance with various aspects disclosedherein;

FIG. 6 illustrates a schematic diagram of an external view of an examplesystem comprising a mobile electronic nose device in accordance withvarious aspects disclosed herein;

FIG. 7 illustrates an example methodology for gathering and analyzing asample in accordance with various aspects disclosed herein;

FIG. 8 illustrates an example methodology for gathering and analyzing asample including receiving input and analyzing a sample with thereceived input in accordance with various aspects disclosed herein;

FIG. 9 illustrates an example methodology for determining a source of asample including connecting to a server in accordance with variousaspects disclosed herein;

FIG. 10 illustrates a high-level functional block diagram of an examplemobile electronic nose device in accordance with various aspectsdisclosed herein;

FIG. 11 illustrates an example schematic block diagram of a computingenvironment in accordance with this specification in accordance withvarious aspects disclosed herein;

FIG. 12 illustrates an example block diagram of a computer operable toexecute various implementations described herein; and

FIG. 13 illustrates an example methodology training a model inaccordance with various aspects disclosed herein.

DETAILED DESCRIPTION

Various aspects or features of this disclosure are described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. In this specification, numerousspecific details are set forth in order to provide a thoroughunderstanding of this disclosure. It should be understood, however, thatcertain aspects of disclosure may be practiced without these specificdetails, or with other methods, components, materials, etc. In otherinstances, well-known structures and devices are shown in block diagramform to facilitate describing this disclosure.

Systems and methods disclosed herein relate to detection of odors,chemicals and/or gasses via handheld electronic devices. In oneimplementation, a mobile device receives, determines and identifies asource of a headspace of a sample. The mobile device is an electroniccomputing device such as for example a smartphone, tablet, PDA, laptop,cookware with circuitry, cooking utensils with circuitry, and the like.

In one embodiment, the mobile device passively receives a sample such aschemicals, gas, or odors. The sample can be received through openings ina body of the mobile device. In another embodiment, a mobile deviceactively draws in the sample via a sample delivery component. The sampledelivery component can include an intake device such as a fan (e.g.,bladed fan, airfoil fan, and the like), or a manually powered pump, forexample. The intake device can pull air from outside the mobile devicecreating a positive pressure inside the device relative to the outsidepressure. This causes air to pass through the device.

A detection component, coupled to a memory and a CPU, of the mobiledevice can receive a sample of the air. The detection component cananalyze a headspace (portion) of the sample. The detection component candetect presence and amount of chemicals in the headspace. In oneimplementation, the detection component can include a sensory array. Thesensory array can react to various chemicals within the headspace. Thereaction can cause a change in physical or electrical properties of thesensory array. In one example, absorption of the chemicals in theheadspace causes physical alterations of the various sensors in thesensory array. Each sensory array can react differently to the variouschemicals. A CPU can transform the reactions of the sensory array into adigital signal. The digital signal can be computed based on astatistical model. For example, in one non-limiting embodiment, anorganic ultra-thin transistor chemical sensor having a channel thatconsists of one or a few monolayers can be employed. The organic thinfilm transistor chemical sensors can have nearly monolayer thin filmchannels that act as highly-sensitive detectors of trace levels oforganic vapors and can perform quantitative vapor analysis. The organicultra-thin film can be permeable to a chemical analyte of interest.

A memory can store digital signals associated with sources (e.g., arose, a foodstuff, burning foodstuff, etc.). In one embodiment, thedetection component compares the digital signal associated with theheadspace to the stored digital signals within the memory. The detectioncomponent can then find the best match and determine the source of theheadspace. In another embodiment, the mobile device can compare thedigital signal associated with the headspace to a memory of a server,such as a server connected via cellular communication networks,intranet, internet, or similar communication networks known in the art.

In another example, the detection component can determine that a bestmatched sample is not in memory. In this case, a new source isassociated with the digital signal associated to the headspace. An inputcomponent can receive information about the source. The memory can storethe associated source with the digital signal.

In another example, the mobile device can receive a plurality ofheadspaces associated with the same sample via the sample deliverycomponent. The detection component can normalize the plurality ofheadspaces into a normalized headspace. The normalized headspace can beanalyzed as above.

In another embodiment, input component can receive information about asource of a sample. In one example, the information can include text,location information (via global positioning satellites, user input,wireless access points, wired access points, etc.), audio information,and/or image information. The detection component can analyze thereceived information and the headspace to determine the source of thesample. In one implementation, the information received by the inputcomponent can narrow the possible sources to be associated with theheadspace to be of a certain genus or type. For example, a voicecapturing device can receive audio and determine that the audio containsa phrase such as “identify this flower”. Thus, the detection componentnarrows the possible sources to flowers.

In another implementation, the detection component can detect if a foodsubstance is expired, not expired, or the quality. For example, thedetection component can determine if milk or wine has gone bad bycomparing an analyzed headspace's associated digital signal to a knowndigital signal. In one aspect, input information can be received as textor audio information such as “is this wine spoiled?” and the detectioncomponent can reduce the amount of digital signals to compare to ananalyzed headspace's digital signal.

In one example, the input component can include an image capturingdevice (e.g., a camera) can capture an image of a source associated witha sample and send the image to the detection component. The detectioncomponent analyzed the image of the source and the headspace of thesample associated with the source. The determination of the source canbe enhanced and/or speed-up through the dual analysis of the capturedimage and the headspace. As one example, the captured image can narrowthe possible sources of the headspace. For example, a sample of thefragrance of a flower can be received and an image of the flower can becaptured. The detection component can determine the headspace isassociated with a flower via analysis of the image of the flower.

Some non-limiting examples of types of sensors or detectors that can beemployed in connection with identification of samples include: acalorimeter, a conductivity sensor, an enzymatic sensor, a biosensor, achemical sensor, an Enzyme-Linked Assay sensor (e.g., an Enzyme-LinkedImmunosorbent Assay (ELISA) sensor), an infrared (IR) spectroscopysensor, a Nuclear Magnetic Resonance (NMR) sensor, an optical sensor, apermittivity sensor, a gas sensor, a Radio Frequency (RF) sensor, anelectronic tongue sensor, a multi-frequency RF sensor, a cantileversensor, an acoustic wave sensor, a piezoelectric sensor, a responsivepolymer-based sensor, a quartz microbalance sensor, a metal oxidesensor, an X-ray Fluorescence (XRF) sensor, a nucleic acid-based sensor(e.g., a DNA-, RNA-, or aptamer-based sensor), or a regenerable sensor.

Furthermore, it is to be appreciated that multiple modalities can beemployed in connection with converging on identification of a sample.For example, image or video capture components of a mobile device can beemployed to identify item(s) of interest to be analyzed, audio analysis,voice analysis, text, can be employed in connection with determiningidentification goals of a user as well as determining properties ofitems that are analyzed. A user can take an image of an item (e.g., asnack) and utter, “is this allergen safe?” The image can be analyzed(e.g., using pattern recognition) to identify that it is a cookie aswell as likely type of cookie (e.g., peanut butter). Based on theutterance, the system determines that the user is interested in ensuringthat the cookie does not include items that may cause an allergicreaction (e.g., nut allergy). The electronic nose can be employed todetect presence of nuts in the cookie or any other potential allergenthat might affect the user. Accordingly, the combination of pattern,voice and smell detection can provide a higher confidence levelregarding item and goal determination as compared to using just onesensing modality.

Moreover, geographic location, time of day, season, etc. can also beemployed in connection with facilitating identification. For example, aglobal positioning system (GPS) component of the mobile device canprovide geographic location, and such information coupled with temporalor season information can facilitate factoring likelihood of gases,chemicals, substances, compounds, allergens or the like that have a highor low probability of presence at such location and time. If the mobiledevice is located in Ohio during the month of May, likelihood of certainallergens (e.g., tree and grass pollens) can be factored into adetermination of presence of certain items of interest. Likewise, if themobile device is located in the Arctic Circle, and the device is locatedoutside the likelihood of a live plant or animal being a source of anitem is relatively low. In yet another example, identification oflocation within a particular restaurant can also be employed tofacilitate item identification. If the restaurant is an Indianrestaurant as compared to a steak house, the presence of certain exoticspices (e.g., turmeric, saffron, garam masala, cumin, coriander, etc.)is likely to be higher than in the steak house.

Embodiments disclosed herein can leverage multiple modalities (e.g.,image or pattern recognition, location based services, web-based searchtools, electronic noses, chemical sensors, audio recognition, time,date, season, location, etc.) to facilitate converging on user itemidentification goals as well as item identification.

Sensors can be self-cleaning (e.g., vibration, light, chemical or gaswashes, etc.) as well as disposable.

In the smelling process of the human olfactory system, the initial stepis to bind specific odorants to the olfactory receptor protein thattriggers signal transduction in a cell. Olfactory receptors expressed inthe cell membranes of olfactory receptor neurons are responsible for thedetection of odorant molecules. That is, when the odorants bind to theolfactory receptors as described above, the receptors are activated. Theactivated olfactory receptors are the initial player in a signaltransduction cascade, which ultimately produces a nerve impulse, whichis transmitted to the brain. These olfactory receptors are members ofthe class A rhodopsin-like family of G protein-coupled receptors(GPCRs). In accordance with an embodiment, an olfactoryreceptor-functionalized transistor is provided, that is useful for abioelectronic nose which can detect and analyze specific odorants withhigh selectivity, by functionalizing a nanostructure transistor with anolfactory receptor (e.g., a lipid membrane having an olfactory receptorprotein is formed to cover surfaces of a source electrode, a drainelectrode, and a nanostructure).

The olfactory receptor protein belongs to a family of G-protein coupledreceptors and may exist over the surface of, the interior of, or thesurface and interior of a lipid double membrane. An olfactory receptormembrane generally includes an ionizable cysteine residue and exists ina conformational equilibrium between biophysically activated andnon-activated states. The activated and non-activated states of theolfactory receptor molecule are associated with a negatively-chargedbase form and a neutral acid form of cysteine, respectively. Whenspecific odorants bind to olfactory receptor molecules, equilibrium ofreceptor molecules moves to an activated receptor form having negativecharges. The negative charges of the olfactory receptor molecules whichwere changed into an activated state modulate contact resistance betweenmetal electrode and nanostructure, leading to reduction in conductance.In accordance with an embodiment, odorant molecules can be detectedhighly selectively based on electrostatic perturbation of ananostructure junction generated from a conformational change by bindingodorants to olfactory receptor molecules. Thus, highly-specificdetection of odorants with femtomolar sensitivity can be achieved inreal time, and various and novel applications such as a highly selectiveartificial nose application can be achieved. In one embodiment, thenanostructure may be at least one form selected from the groupconsisting of nanotube, nanowire, nanorod, nanoribbon, nanofilm, andnanoball. For example, semiconductor nanowires such as siliconnanowires, and carbon nanotubes may be used, and a single-walled carbonnanotube can provide desirable high biocompatibility and devicecharacteristics.

In another embodiment, a random network of single-walled carbonnanotubes (SWCNTs) coated with non-polar small organic molecules inconjunction with learning and pattern recognition algorithms (e.g.,artificial neural networks, multi-layer perception (MLP), generalizedregression neural network (GRNN), fuzzy inference systems (FIS),self-organizing map (SOM), radial bias function (RBF), geneticalgorithms (GAS), neuro-fuzzy systems (NFS), adaptive resonance theory(ART) and statistical methods including, but not limited to, principalcomponent analysis (PCA), partial least squares (PLS), multiple linearregression (MLR), principal component regression (PCR), discriminantfunction analysis (DFA) including linear discriminant analysis (LDA),and cluster analysis including nearest neighbor) can be employed. Forexample, detection of volatile compounds as biomarkers for diagnosis ofmedical conditions can be performed using olfactometry systems thatperform odor detection through use of an array of cross-reactive sensorsin conjunction with pattern recognition algorithms. Each sensor can bewidely responsive to a variety of odorants. Each analyte can produce adistinct signature from an array of broadly cross-reactive sensors. Thisconfiguration allows to considerably widen the variety of compounds towhich a given matrix is sensitive, to increase degree of componentidentification and, in specific cases, to perform an analysis ofindividual components in complex multi-component mixtures. Patternrecognition algorithms can then be applied to the entire set of signals,obtained concurrently from a set (e.g., one or more) of sensors in thearray, in order to glean information on identity, properties andconcentration of vapor exposed to the sensor array.

In one or more embodiments, a smell or scent can be identified based ona unique vibration signature. For example, a micro spectrometer can beemployed to measure a vibration signal of received or gathered moleculesor particles, such as molecules of a headspace. A micro spectrometer cananalyze gas or molecules using spectrometry and identify particlestherein at a very granular level, the spectrographic signature can bematched to database of known scent signatures to identify scentassociated with the sample. Additionally, the signature can be fed intoa trained machine learning system (MLS) to perform the scentdetermination.

According to the vibrational theory a smell of a molecule is determinedby intramolecular vibrations (frequency and amplitude), rather than bythe shape of the molecule. Thus, a micro spectrograph can be utilized todetermine vibrations of molecules within a headspace. The vibrations canbe cross referenced with kwon vibrations to identify gases or moleculeswith the headspace.

Referring now to FIG. 1, there is illustrated a non-limiting exemplaryimplementation of a system 100 in accordance with various aspects ofthis disclosure. The system 100 can include mobile device 110 that caninclude a sample delivery component 112, a detection component 120, acomputer processing unit (CPU) 130, and a memory 140. The mobile device110 provides for gathering samples, and detecting and identifying sourceof a sample. The mobile device 110 receives sample 116 associated with asource 114. Sample 116 can be an odor, chemical, and/or airbornefragrance given off by source 114. In one aspect, CPU 130 is capable ofexecuting various components and/or portions of components stored in acomputer readable memory 140.

In some embodiments, sample delivery component 112 and/or detectioncomponent 120 can be modularly connected with mobile device 110. Forexample, a sample delivery component 112 can be comprised in a modulardevice that can communicatively connect or couple with mobile device 110(e.g., via an interface, such as a universal serial port (USB), MicroUSB, Mini USB, Lightning™ port, Bluetooth™, etc.). In another aspect, amodular devices can be removably connected to mobile device 110, such asthrough a locking mechanism (e.g., latch, clip, etc), magneticconnection, Velcro™ connection, threaded connection, or other variousforms of connection.

The sample delivery component 112 can receive the sample 116. In oneimplementation, the sample delivery component 112 passively receives thesample 116 as the sample 116 diffuses. In another implementation, thesample delivery component 112 actively gathers the sample 116. Forexample, sample delivery component 112 can comprise an intake componentthat draws air into the mobile device 110 or a portion of the mobiledevice by creating a negative air pressure in the mobile device 110 orthe portion of the mobile device relative to an external air pressure.

The sample delivery component 112 is in fluid communication with thedetection component 120. Detection component 120 can receive the sample116 and analyze a headspace of the sample 116. Detection component 120can analyze the chemical composition of the headspace or analyze avisual aspect of the headspace.

In one implementation, detection component 120 includes a sensory array.The sensory array can comprise an array of polymer films, each polymerfilm of the array of polymer films can be of a slightly different type.However, it is to be appreciated that various polymer films of the arrayof polymer films may be of a same type. The electrical conductivity ofthe different types of films varies in the presence of differentchemicals, so that when the array of films is exposed to a particularodor, the different films respond in a characteristic way. In anotheraspect, an array of polymers can respectively swell to varying levelswhen exposed to different molecules of a headspace. An aggregateamplitude signals associated with the polymers can generate a result asa unique signature for a particular smell. It is noted that the polymerfilms can be removable or replaceable (e.g., disposable). For example, acartridge can be removed and a new cartridge can be inserted into thedetection component 120.

In an example, detection component 120 can determine when a polymer filmshould be replaced. In an aspect, determining when to replace thepolymer film can be based on passage of time, a number of scentssmelled, a number of attempts to smell scents, degradation in accuracy,and the like.

In another implementation, detection component 120 can include a microspectrometer that can analyze particles to identify the particles ordetermine concentration of different particles. It is noted that anidentified particle or combination of particles can represent a scent. Asignature can be generated from the combination of particles and can beutilized to identify a source.

In some embodiments, a user can provide a known scent to filer from aheadspace. For example, a gathered headspace may contain a scent knownby the user and a scent unknown by the user. The detection component 120can remove the known scent to identify the unknown scent or generate arepresentation of the unknown scent.

In another example, the sensory array can comprise an array oftransistors made out of various semiconductor materials (e.g., siliconoxide sensor). Transistors made of different materials can responddifferently to different chemicals, so that the array produces adistinctive signal when exposed to an odor.

In another implementation, the detection component 120 can includevisual detectors (e.g., a photoelectric detector). Visual detectors cancomprise a light source and a light sensor. The light source produces alight that is aimed at the light sensor. The light sensor can determinewhen the light is blocked. It is to be appreciated that the detectioncomponent can comprise one or more sensors. Further, the sensors cancomprise various sensors such as ionization sensors.

Detection component 120 converts the reactions of the sensory array orthe visual system into a digital signal. The digital signal represents achemical composition of the headspace. The memory 140 can store thedigital signal. The detection component 120 can compare the digitalsignal to various other digital signals stored in memory 140 todetermine an identity of a source associated with the headspace. In oneimplementation, detection component 120 uses at least one of a hashtable, fuzzy logic, artificial neural network (ANN), or patternrecognition modules, for example, to determine an identity of a sourceassociated with the headspace.

In an embodiment, detection component 120 can train a machine learningsystem to identify smells (e.g., sources of a sample). In an aspect, amodel can be trained using any number of samples that are analyzed byhumans. For example, users can provide input identifying a source of asample. For iterations of a user identifying a smell, the smell can bestored in one or more databases. The iterations can be utilized to traina model (e.g., generate a library of smells). The model can increase insize, robustness, and/or accuracy as user input increases. In an aspect,a number of identifiable smells (e.g., representations of samples matchwith a source) can continue to grow indefinitely.

In one or more embodiments, an initial model can be a pre-trained orseeded model that, once launched on a local device (e.g., mobile device110), can continue with its training on a per user/device basis. In anaspect, the model can be customized per owner. It is noted that acustomized (e.g., per user or group of users) model can be storedlocally or remotely (e.g., cloud based storage. In some embodiments, alocal model data (e.g., of mobile device 110) can be shared with aserver to enhance a server model/library. It is noted that a user canprovide input to opt-out of sharing a model.

Now turning to FIG. 2, there illustrated is a non-limiting exemplaryembodiment of a system 200. System 200 can comprise a mobile device 210capable of using input 224 to aid in detecting and/or identifyingvarious odors, chemical compounds, aromas, and or gaseous substances.Mobile device 210 comprises a sample delivery component 212, a detectioncomponent 220, an input component 226, a computer processing unit (CPU)230, an output component 250 and a memory 240. Mobile device 210provides for receiving a sample 206 and detecting or identifying asource 204 associated with sample 206. In one aspect, sample 206 is aportion of air in the proximity of mobile device 210. In another aspect,sample 206 can contain a scent, an odor, chemical(s), airborneparticles, or gaseous substance(s).

In one aspect, CPU 230 is capable of executing various components and/orportions of components stored in a computer readable memory 240. Memory240 can also store a plurality of entries, each entry comprising adigital odor signals, class, and source name, for example. Each entrycan also comprise various other fields such as photo identification,date detected, and location, to name a few.

Sample deliver component 212 can actively or passively receive sample206. Detection component 220 can receive a headspace of sample 206 andanalyze the headspace. Detection component 220 can determine a digitalodor signal associated with the headspace.

Input component 226 can receive and analyze input 224. Input component226 can receive input 224 that can comprise extrinsic data, such asaudio, visual, text, location data, and/or other user input. In oneaspect, input component 226 includes one or more input interfaces suchas a microphone, a camera, a key board, an actuator, a touch screenand/or other user interfaces capable of receiving input 224, forexample. In one aspect, input component 226 can receive input 224relating to source 204 and/or sample 206. For example, input 224 cancontain information relating to a source's class, image, and/orlocation.

In one implementation, input component 226 comprises a microphone. Inputcomponent 226 receives input 224 as audio information via themicrophone. Input component 226 can identify speech in input 224. Memory240 can store the digital signal. For example, a user can say “identifythis flower” and input component 226 can receive the audio as input 224.In one example, input component 226 can convert the audio to a digitalsignal and analyze the digital signal. In the above example, a filtercan be generated to limit possible results to flowers. The filter canfacilitate searching of a specialized model and/or a portion of a modelthat is associated with flowers. Limiting searching of the specializedmodel (or portion of a larger model) can result in a more accurateresult and/or a faster searching/identification process.

The audio can be utilized by detection component 220 to generate acommand and/or extrinsic data associated with receiving a sample. Forexample, detection component 220 can identify a phrase such as“identify,” “analyze,” “add scent,” and the like. The phrase cancorrespond to one or more commands. In an example, if a user knows anidentity of source 204, the user can utter, “add this sent for a rose.”Detection component 220 can determine that the source is a rose and canadd the scent to a library or model. In another example, extrinsic datacan be extracted from the audio. In the above example, the extrinsicdata can be a type or classification of a source, such as a rose. Inanother aspect, a query can be generated based on the extract extrinsicdata. For example, a filter can be generated to limit possible resultsto species of roses.

In another embodiment, input component 226 includes a user interfacedevice, such as a touch screen or keyboard, for example. In one aspect,the user input device can receive input 224 as text. In anotherimplementation, input 224 contains information relating to sample 206and/or source 204. Detection component 220 can extract extrinsic dataand/or commands from the text. The extrinsic data can be utilized togenerate filters. For example, a user can provide the text “find a storewhere I can buy this perfume.” A first filter can comprise “perfume” andcan be utilized to limit possible results to perfumes. A second filtercan comprise “stores” and can be utilized to generate a result thatidentifies stores where the perfume can be purchased. In one or moreother examples, detection component 220 can determine a location of themobile device 210. The location can be utilized as another filter thatfacilitates limiting possible results based on the location. In theabove example, the identified stores can be stores within a determineddistance from the location and/or online stores.

As another example, input component 226 can include a camera. The cameracan capture visual information. The visual information can be receivedby input component 226 as input 224. In one aspect, the visualinformation is an image of source 204. Input component 226 canidentification the image as relating to a class of objects, being aspecific object, and the like. In one implementation, input component226 determines if the image of source 204 is associated with an imagestored in memory 240. For example, an image is captured and inputcomponent 226 determines if the image is an image of a flower.Determining a subject or object in an image can comprise utilizingpattern matching techniques. The pattern matching techniques canidentify a type of object, an identity of the object, and the like. Insome embodiments, a result of the pattern matching can be utilized as afilter that filters possible results.

Detection component 220 receives analyzed input from input component206. The analyzed input can contain extrinsic information relating tosource 204, sample 206, receiving the sample 206, and the like.Detection component 220 applies the analyzed input to narrow and/orimprove identification of source 204 associated with sample 206. Forexample, detection component 220 receives analyzed input containinginformation such as “flower” and then detection component 220 cancompare the analyzed headspace to entries in memory 204 that have aclass type of “flower”. Detection component 220 can limit its comparisonof the digital headspace signal to entries in memory with a classassociation of “flower”. In another example, a filter can be generatedto filter possible results and the filter can be transmitted to a searchengine (and/or utilized to generate a query to a search engine).

In another aspect, detection component 220 can receive input 224 oranalyzed input from input component 206 that contains a plurality ofinformation relating to a source 204 and/or sample 206. For example,input 224 can contain extrinsic data such as a date field, a locationfield, and an image field. In one aspect, input 224 can contain a datefield and location field. Entries in memory 240 can have associated dateranges and location ranges. Detection component 220 can apply the input224 to limit searchable entries. For example, a flower may be indigenousto a certain location and may only bloom during a certain date range.Detection component 220 can apply input 224 to reduce the number ofpossible entries.

In an embodiment, detection component 220 can utilize extrinsicinformation to reduce a number of possible results (e.g. sort knownscents). As noted supra, extrinsic information can include locations,GPS coordinates, seasons (e.g., season of a year), time, date, userinput (e.g., voice, text, etc.), images, and the like. The extrinsicinformation can be utilized to hone in on a match of a collected sampleand the like. In an example, a user can provide a voice command, textualquery, or the like that indicates that the user desires to identify ascent based on a species associated with a source. Detection component220 can then limit a search to a set of candidate scents belonging tothe desired species. In another aspect, detection component 220 canfurther limit candidate scents based on additional extrinsic informationnot provided by a user but rather automatically gathered by detectioncomponent 220. For example, detection component 220 can determine acurrent location (e.g., a restaurant), date, and time and can apply thecurrent location, date, and time to further limit potential candidatescents.

In another example, a user can provide a command “identify thisvegetable dish.” Detection component 220 can determine that the speciesis one or more of food or vegetables. Detection component 220 canfurther determine that the user is in an Italian restaurant (e.g., basedon GPS location, a local access point, other devices, etc.) during themonth of August. Thus, potential candidate scents can be further limitedbased on dishes common to Italian restaurants (or dishes offered by theparticular restaurant) and having a vegetable that is available duringAugust. In another aspect, an image of the dish can even further limitpotential candidate scents. It is noted that the above is but a limitedset of examples; as such, detection component 220 can utilize virtuallyany criterion (extrinsic data) or combination of criteria to limitcandidate scents, improve accuracy, decrease processing time, orotherwise alter performance.

As another example, a user can identify a smell and system 200 cancollect a headspace, perform an analysis (e.g., locally or through acloud-based analysis), and apply filters to facilitate generation of asearch query. The search query can be transmitted to a search engine togenerate a result. The result can comprise an identification of sources,scent signature matches, confidence scores, solutions/cures, backgroundinformation of sources, and the like. In some embodiments, a searchengine (e.g., utilizing one or more models) can generate a confidencescore and provide the confidence score to a user (e.g., 99% confidentthat a scent includes garlic and onion).

In one or more embodiments, system 200 can be configured to performspecialized functions or limited detection of substances. In an aspect,system 200 can be pre-configured to identify a number of scents. Inanother aspect, system 200 can apply constraints (e.g., via sampledelivery component 212 or detection component 220). For example,detection component 220 can be configured to limit a number of possiblesubstances based on a detection mode. As described herein, detectionmodes can comprise one or more of a “dangerous substance mode,” “huntingmode,” “allergy mode,” “breath mode,” “cooking mode,” or the like.

For example, in a dangerous substance mode, detection component 220 canlimit searchable substances (e.g., representations of substances) to adefined list of substances, such as carbon monoxide, smoke, otherharmful gases, toxins, etc. In an aspect, a dangerous substance mode canbe triggered based on user input (e.g., manually entering the mode) orautomatically (e.g., based on a trigger). For example, a dangeroussubstance mode can be triggered during certain times of day (e.g., suchas at night when a user typically sleeps), based on current locations(e.g., while in a car, boat, a garage, next to a bed, etc.), based on acharging status of mobile device 210 (e.g., users typically charge aphone in and stay near the phone), and the like.

In an allergy mode, system 200 can be configured to identify a limitednumber of sources that a user is allergic to, potentially allergic to,or otherwise adverse to. For example, a user can provide inputidentifying that the user is allergic to nuts. In an allergy mode,detection component 220 can limit a number of searched headspaces toheadspaces associated with nuts. It is noted that the allergy mode canbe triggered manually or automatically. For example, a user can provideinput to trigger the allergy mode to detect presence of an allergen. Inanother example, a user may experience an allergic reaction (e.g., fromfood, seasonal, dust, etc.) and not know the cause of the allergicreaction. The user can trigger the allergy mode to facilitate detectioncomponent 220 identifying a source associated with a headspace andproviding a list of possible allergens. In accordance with variousembodiments disclosed herein, detection component 220 can storeidentified sources and compare the identified sources to determine acommon possible allergen. In another example, detection component 220can utilize extrinsic information (e.g., location, date, time, etc.) todetermine the possible allergens.

As another example, system 200 can be configured to facilitateperformance of a “breath mode.” In a breath mode, detection component220 can discern a level of bad or good breath. In an aspect, a level ofbad or good breath can be based on subject analysis or objectiveanalysis. Subjective analysis can comprise an analysis based on userinput or historical data indicative of past user input or actions.Objective analysis can comprise an analysis based on heuristics such asidentification of particular headspaces, analysis of concentration of aparticular odor, or the like. In an aspect, a user can provide input totrain a model, such as breathing when a level of good or bad breath isknown and providing input identifying the level of good or bad breath.As above, other extrinsic information can be utilized to determine alevel of good of bad breath. For example, during a first time period(e.g., morning hours) the breath mode can analyze a headspace todetermine whether a coffee odor is present. It is noted that a user cantrigger a breath mode based on interaction with an interface (e.g.,button, touch screen, voice command), breathing into or about mobiledevice 210 (e.g., a microphone can detect the user breathing into themobile device), and the like.

In another example, system 200 can be configured to facilitateperformance of the breath mode to diagnose a user. For example, inbreath mode, a user can breathe into sample delivery component 212 anddetection component 220 can discern a blood sugar level, such as basedon a presence or concentration of ketones (e.g., acetone). Ketoneslevels increase when there is insufficient amounts of insulin to drivecells. Typically, diabetics exhibit higher levels of ketone accumulationand ketone levels in a user's breath can be correlated to blood glucoselevels.

In some embodiments, detection component 220 can generate alerts orsuggestions based on a triggering event. For example, detectioncomponent 220 can detect a sneeze or other trigger and can perform asearch of a headspace to identify potential causes of the sneeze orother trigger. In an example, detection component 220 can search aheadspace for the presence of dander, pollen, representations of plants,and the like to facilitate a possible cause of the sneeze. An alert canbe generated to inform a user of the presence of an identified sourceand offer a possible correlation, such as the user may be allergic tocats. In some embodiments, system 200 can include a photo sensor thatcan identify a level of light and/or change in a level of light. If achange in lighting is detected, detection component 220 can determinethat the change in light may be a cause of a sneeze. In another aspect,detection component 220 can generate an output identifying possiblecauses and allow the user to select a possible cause.

In various embodiments, detection component 220 can determine asolution, warning, suggestion, or other descriptive data associated witha scent based on identification of substances and/or extrinsicinformation. For example, a user can take a picture of source and/orutter a command to identify the chemical makeup of the source (e.g.,snaps a picture of a blade of a weed and utters identify this weed). Thecombination of pattern recognition coupled with headspace analysis canfacilitate identifying accurately the type of plant as well as providesupplemental information regarding allergic properties, best ways oferadicate the weed, potential harmful or beneficial properties, or thelike. In another embodiment, detection component 220 can detect asubstance and provide a potential solution. For example, radon gas,leaking oil or gas, or other chemicals, body odor, foot odor, type ofbacteria associated with particular odors can be detected and solutionsto kill such bacteria can be generated.

An alert can be generated based on a concentration, degree, or presenceof a substance, such as an airborne pathogen. For example, detectioncomponent 220 can determine a level or concentration of an airbornepathogen based on an analysis of a headspace. If the pathogen has a highconcentration (e.g., above a certain threshold) the system 200 can alerta user to prevent developing an illness or sickness (e.g., the flu). Insome embodiments, system 200 can share locations of high concentrationsof airborne pathogens. The shared locations can facilitate generation ofa map of known high concentrations. In an aspect, as multiple systemsshare locations, a map can be updated and enhanced. In another aspect,the map can be utilized to alert a user who is heading towards an area(e.g., location) identified as being associated with a highconcentration of an airborne pathogen.

It is noted that the system 200 can identify scents and the relativeconcentration of respective sources (e.g., 75% roses, 10% lilac, 5%honeysuckle, and 10% miscellaneous or unknown sources) through analysisof a headspace. For example, detection component 220 can analyze aheadspace and determine whether one or more sources are identified atleast by a portion of the headspace. The detection component 220 candetermine a concentration based on a ratio comparison of the headspace.

In another embodiment, detection component 220 can determine a directionof a scent and can determine a navigational path to a likely source ofthe scent. It is noted that detection component 220 can include anaccelerometer, gyroscope, GPS component, or the like. In someembodiments, sample delivery component 212 can receive or gathermultiple samples to determine a location or direction of a scent. Forexample, a user can walk in an environment and smell food, such as abarbeque scent, which the user desires to locate. Sample deliverycomponent 212 can gather samples as a user moves and detection component220 can determine whether a concentration of the scent is altered (e.g.,increase or decreased). In one aspect, sample delivery component 212 cangather samples periodically (e.g., based on passage of time, distancetraveled), randomly, semi-randomly, based on user commands, or the like.As samples are gathered and/or analyzed, the concentrations of a scentcan be utilized to determine a direction for a user to travel in orderto locate the scent. For example, if a concentration or intensity isincreasing, the user may be traveling in the proper direction. Inanother example, a user can smell some foul odor in a building or otherenvironment. The user can utilize mobile device 210 to detect thelocation of the foul smell and remove the smell (e.g., spoiled food,rotten garbage, and the like).

In other embodiments, other users can mark a location of the scent and,in response to detection component 220 detecting the scent, the locationor source can determined based on the other users marking of thelocation of the scent. Once a location is determined, mobile device 210can facilitate generating navigational directions to the location. It isappreciated that other extrinsic information can be utilized to identifya location or origin of a scent. For example, detection component 220can determine scent is from a BBQ restaurant can look up or search fornearby restaurants serving BBQ food.

In an example, detection component 220 can facilitate identifyinganimals (e.g., prey or predators) in an area and inform a user or hunterof types of prey in the area (e.g., whitetail deer, wild boar, rabbit,turkey, ducks . . . ) or types of dangerous animals in the area (e.g.,bears, wolves, etc.). As above, intensity of a scent and/or dander of ananimal can be monitor such that the user can be guided to areas havinggreater intensity or concentrations. In one aspect, a threshold can beutilized to alert a hunter when an intensity is reached. For example, ifa threshold is set to a high intensity the hunter can be alerted tosignify the presence of an animal in a nearby vicinity. In anotherexample, if the threshold is low, the hunter can be alerted that theanimal may be moving or has left a vicinity. In some embodiments,detection component 220 can generate a scent marker that marks alocation (e.g., GPS coordinates) of a particular scent, identifiedsources, and time-stamps the mark. The markers can be shared by otherhunters and/or used to develop a map of animal trails and migrationpatterns. In another example, a hunter can capture an image of an animaltrack (e.g., via a camera), such as a footprint or hoof print, anddetection component 220 can determine an animal associated with thetrack.

In another aspect, a particular source or scent can be filtered from aheadspace. In an example, detection component 220 can detect one or morescents in a headspace. A particular scent can dominate or corrupt otherscents. If the dominant scent is identified, it can be removed or filterto identify other scents. For example, exhaust fumes from a car can beidentified and filtered from a headspace. In an aspect, filtering caninclude subtracting the representation of the exhaust fumes from aheadspace. The residual scent can then be determined.

In another example, a user can provide input to identify a perfume anddetection component 220 can detect the perfume and identify otherdescriptive information associated with the perfume. For example,information describing the perfume, a maker of the perfume, nearbylocation where the perfume can be found (e.g., such as a nearby retailstore, an online store, and the like), a popularity of the perfume(e.g., based on a number of identifications associated with theperfume), or other information associated with the perfume.

In yet another embodiment, the system 200 can effect purchases ofproducts that are determined to be associated with a detected andidentified scent. For example, a user can utter a command such asidentify that perfume, the detection component 220 can detect andidentify that perfume. The user can then utter purchase the perfume andhave it delivered to my home. The system 200 can automatically effectpurchases of items associated with the identified scent. Purchases caninclude for example: plants, perfumes, colognes, spices, foods,detergents, cleaning supplies, scented oils, waxes, candles, and thelike (e.g., items that have particular scents associated therewith).

Referring now to FIG. 3, there illustrated is a non-limiting exemplaryembodiment of a system 300. System 300 can primarily comprise mobiledevice 310 in accordance with various aspects of this disclosure. Mobiledevice 310 comprises sample delivery component 312, detection component320, input component 326, processing unit 330, memory 340, and outputcomponent 350.

Mobile device 310 can receive a sample 306 associated with a source 304via a sample delivery component 312. Input component 326 can receive andanalyze input 324 containing information relating to sample 306 and/orsource 304. Detection component 320 can receive analyzed input and aheadspace of sample 306. Detection component 320 can analyze theheadspace in conjunction with the analyzed input.

In one aspect, processing unit 330 is capable of executing variouscomponents and/or portions of components stored in a computer readablememory 340. Memory 340 can also store a plurality of entries, each entrycomprising a digital odor signals, class, and source name, for example.Each entry can also comprise various other fields such as photoidentification, date detected, and location, to name a few.

In another aspect, mobile device 310 is in communication with one moreserver(s) 360. Communications can be facilitated via a wired (includingoptical fiber) and/or wireless technology. Server(s) 360 comprise one ormore server data store(s) that can be employed to store informationlocal to server(s) 360. In one implementation, server(s)'s 360 datastore(s) contains one or more entries, each entry relates to uniquesources with associated information, such as class, source name, digitalidentification, and image, for example.

In one implementation, detection component 320 receives entries and/orinformation relating to entries from server(s) 360. In another aspect,detection component 320 searches entries in server(s)'s 360 data store.

In another implementation, detection component 320 can send informationrelating to source 304 and or sample 306 to server(s) 360. Server(s) 360can record information in the server data stores.

Output component 350 can output information. In one embodiment, outputcomponent 350 includes one or more output devices such as a speaker,and/or display. Output component 350 outputs information relating tosample 306, source 304 and a detection result. A detection result caninclude information relating to source 304 such as a determined identityand/or class.

In one or more embodiments, system 300 can utilize a cloud-basedanalysis or a local-based analysis of received data (e.g., a headspace).For example, sample 306 can be received by sample delivery component 310and detection component 320 can generate a representation of the sample.A representation of the sample can be a spectrogram, a hash (e.g., basedon a hash function), or the like. In some embodiments, system 300 cananalyze the representation of the sample based on a locally connecteddatabase or library of representations of samples. In another aspect,input component 326 can receive a library of representations (or portionthereof) from server 360 or from another external storage device.

In another example, output component 350 can transmit a representationof a sample to an external device (e.g., server 360). The externaldevice can analyze the representation of the sample, can transform therepresentation of the sample, and the like. For instance, a server canreceive a representation of a sample and generate a hash of therepresentation. The server can then determine whether the hashcorresponds to an entry in a hash table. If the hash does correspond tothe entry, then data associated with the entry can be received by inputcomponent 326 and/or utilized to identify a source.

In embodiments, a sample can be analyzed in one or more phases, such asan initial analysis and a secondary analysis. As an example, mobiledevice 310 can perform an initial analysis that is constrained by one ormore factors. The factors can include an amount of time (e.g.,processing time), a number of entries in a local database, performancemetrics of mobile device 310, and the like. For example, an initialanalysis can be configures such that a result is generated within apredetermined time period. In another example, the initial analysis canbe limited to a determined complexity based on a processing speed ofprocessing unit 330. The secondary analysis can comprise a more detailedor stringent analysis. For example, the output component 350 cantransmit a result of the initial analysis, a representation of thesample, and/or other data (e.g., user input, images, etc.) to server360. Server 360 can perform a more detailed or stringent analysis todetermine a source of the sample. In an aspect, the server 360 cancompare the representation of the sample with a larger library ofrepresentations and the like.

In an implementation, mobile device 310 can share models (e.g.,libraries) and/or utilize models in a cloud-computing environment. Forexample, mobile device 310 can share (e.g., transmit) a local library toother mobile devices or server 360. In an aspect, multiple libraries canbe aggregated and a robust library can be generated. In someembodiments, a shared library can be based in part on extrinsic datasuch a location, date, time, and the like. For example, every timemobile device 306 gathers a sample, location data (e.g., GPS location,date, time, etc.) can be recorded and/or attached to a model. Thelocation data can be matched with or location data to provide a moredetailed and/or improved model.

In another aspect, detection component 320 can determine inaccuraciesand/or anomalies within a model. For example, detection component 320can cross-reference stored scents of a model with stored scents of oneor more models. If detection component 320 determines that arepresentation of a sample matches another representation of a samplebut identified sources do not match, then detection component 320 canmark the discrepancy for further analysis, generate a notification,correct the discrepancy, or otherwise process the discrepancy. In oneexample, detection component 320 can determine confidence scoresassociated different sources. A source having the highest confidencescore can be chosen and the discrepancy can be resolved by replacing thesource associated with the lower confidence with the source associatedwith the chosen confidence score. In another example, if a confidencescore is not above a threshold confidence (e.g., 99%) and/or is notsufficiently greater than a confidence score associated with anothersource (e.g., 50% greater) then neither source is chosen. If neithersource is chosen, entries in a model or library can be marked as entriesneeding additional data, entries having conflicting data, entries thatare potential inaccurate, and the like.

In some embodiments, detection component 320 can identify a useridentity as having a poor, advanced, or other level of sense of smell.For example, based on a history of scents and user input, detectioncomponent 320 can determine whether the user properly identifies scents.If the user has a threshold number of incorrect or inaccurateidentifications, then the user can be identified as a user having a poorsense of smell. Entries of a model associated with a user having a poorsense of smell can be removed to increase accuracies of a model. Inanother aspect, a weighting system can utilize a level of sense of smellassociated with users to generate a weighted score of entries associatedwith the users. For example, entries of a model associated with usershaving an advanced sense of smell can be given a greater weight thanthose associated with less advanced users.

In an example, mobile device 310 can utilize a specialized model that istrained by an expert, a set of experts, or other quality determiningentities. Experts or quality determining entities can be focused along acertain field such as wine, spirits, perfumes, fruits (or foods ingeneral), or any other field. For example, a set of experts can identifywines and quality associated with the wines. The qualities and scentscan be utilized to train a model specifically designed to determine aquality of a wine. In another aspect, a specialized model may utilize

In some embodiments, system 300 can share (e.g., via input component 326or output component 350) identified scents and a review of a sourceassociated with the identified scent. For example, a user can provide areview associated with the scent, such as a rating, “liking,” text basedreview, or other input. The review can be transmitted and/or shared withother devices. In another aspect, user reviews can be utilized to trainuser generated models and/or generate leader boards associated withscents or particular fields of interest. For example, users, via mobiledevices, can gather scents of a particular type of consumable productand votes for the products can generate leader boards.

Referring now to FIG. 4, there illustrated is a system 400 which gathersa sample, delivers a sample, and/or removes a sample from a device inaccordance with various aspects of this disclosure. Sample deliverycomponent 402 can include am intake component 410, an intake line 414,an outtake line 422, an outtake component 430. A controller 450 canfacilitate operation of sample delivery component 402 and various othercomponents in accordance with this disclosure, such as detectioncomponent 440.

Intake component 440 gathers or receives a sample 460. In one aspect,intake component 440 passively receives sample 460 as sample 460diffuses through airspace. In another aspect, input component 440includes a mechanical device that can draw in sample 460. The mechanicaldevice can include a bladed fan, a bladeless fan, or other known devicescapable of drawing in air as known in the field.

In another aspect, intake component 410 can comprise one or moreapertures in a mobile device. Sample 460 can enter the mobile devicethrough the one or more apertures.

Intake line 414 can transfer or provide a passage to various componentsin accordance with this disclosure, such as detection component 440, forexample. Intake line 414 can be in fluid communication with detectioncomponent 440, for example. Intake line 414 can comprise tubing, orother device of plastic, rubber, metal, or other suitable means as knownin the art. Detection component 440 can analyze sample 460 or aheadspace of sample 460.

Outtake line 422 can fluidly connect various components, such asdetection component 440, to outtake component 430. Outtake Air 470 canpass through outtake line 422 and exit the mobile device through outtakecomponent 430. Outtake component 430 can comprise one or more aperturesto allow the spent sample 460 or other outtake air 470 to exit themobile device.

In one aspect, outtake line 422 can comprise tubing, or other device ofplastic, rubber, polymer, ceramic, metal, or other suitable means asknown in the art.

In one implementation outtake component 430 can comprise a mechanicaldevice for drawing a sample. The mechanical device can include a bladedfan, a bladeless fan, or other known devices (e.g., micro electromechanical systems (MEMS) devices) capable of drawing in air as known inthe field. For example, a system containing sensors and microjet MEMSactuators can be employed to exact some flow control, and synthetic jetscan be employed to reduce drag and modify flow over air foils and bluffbodies. In one aspect, outtake component 430 and intake component 410can each comprise one or more mechanical device. In anotherimplementation, intake component 410 or outtake component 430 canutilize the same one or more mechanical devices. In another aspect,outtake component 430 can include a mechanical device that causes air topass through input component 411 and output component 430.

In another implementation, outtake component 430 and intake component410 can both utilize the same one or more apertures to allow air toenter and exit a mobile device.

Referring now to FIG. 5, there illustrated is a schematic diagram of anexemplary system 500 in accordance with various aspects of thisdisclosure. In accordance with various aspects of this disclosure,system 500 can comprise sample delivery component 502. Sample deliverycomponent 502 can gather a sample in air space, deliver the sample tovarious components in a mobile device, and remove the gathered samplefrom the mobile device.

Sample delivery component 502 can comprise one or more intake apertures510, fan 520, motor 524, intake duct 530, outtake duct 540, and one ormore outtake apertures 550. A controller 570 can control various aspectsof sample delivery component 502. Further, a power supply 560 can powervarious aspects of sample delivery component 502 such as fan 524, forexample.

A sample can be received through one or more intake apertures 510. Fan520 can draw in the sample. Likewise, motor 524 can receive power frompower supply 560. As fan 520 rotates, it creates a low pressure area inthe mobile device with respect to the airspace outside the device. Airis then caused to enter the one or more intake apertures 510.

The sample can pass through intake duct 530. Intake duct 530 can be incommunication with various components, such as detection component 536.The sample can also pass through or be forces through outtake duct 540.The spent sample can then exit through one or more outtake apertures550.

In other implementation, the intake duct 530 and outtake duct 540 can beof one unitary construction or modular construction. Likewise, powersupply 560 can be a battery, fuel cell or other power source. Powersupply 560 can be within sample delivery component 502 or can be a powersupply for a larger mobile device. In another aspect, the one or moreintake apertures 510 and one or more outtake apertures 550 can comprisethe same one or more outtake apertures.

Turning now to FIG. 6, there illustrated is an exemplary schematicdiagram of a system 600. System 600 can comprise a mobile device inaccordance with this disclosure, as seen from a front view 602 and aback view 640. The mobile device includes a display 610, a microphone620, a housing 630, a camera 650, a first at least one opening 660 and asecond at least one opening 670.

Housing 630 comprises a shell or enclosure that houses variouscomponents in accordance with the claimed subject matter. Housing 630can be made of a unitary or multi-piece construction and can consist ofone or more of metal, glass, plastic, ceramic, polymer, wood, and othermaterial known in the art.

In one aspect, display 610 can be a touch screen, monitor, digitaldisplay, and or other screen as known in the art. Display 610 canreceive input from a user in accordance with various aspects of thisspecification. For example, display 610 can receive input regarding asample of an odor, airborne gas or chemical and can receive commandsthrough user interaction.

In one implementation, microphone 620 can receive user input. Forexample, microphone 620 receives audio from a user such as “identifythis flower”. Various components in this disclosure can receive capturedinput, for example, an identification component can receive a capturedimage.

In another aspect, camera 650 can receive and or capture visual input.For example, camera 650 can be pointed at a source object. Camera 650can capture an image or images of the source object. Various componentsin this disclosure can receive captured input, for example, anidentification component can receive a audio input

In another aspect, the first at least one opening 660 can serve as anopening for a speaker, a heat ventilation and/or an intake for a sampledelivery component in accordance with various aspects of thisdisclosure. In one implementation, the first at least one opening 660comprises a plurality of slits, openings, or apertures in housing 630.

Similarly, the second at least one opening 670 can serve as an openingfor a speaker, a heat ventilation and/or an outtake for a sampledelivery component in accordance with various aspects of thisdisclosure. In one implementation, second at least one opening 670comprises a plurality of slits, openings, or apertures in housing 630.

In another implementation, the first at least one opening 660 and thesecond at least one opening 670 can comprise the same at least oneopenings. Thus, the amount of openings can be reduced.

Referring now to FIGS. 7-9 and 13, there are illustrated methodologiesand/or flow diagrams in accordance with the disclosed subject matter.For simplicity of explanation, the methodologies are depicted anddescribed as a series of acts. However, acts in accordance with thisdisclosure can occur in various orders and/or concurrently, and withother acts not presented and described herein. Furthermore, not allillustrated acts may be required to implement the methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methodologies couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be further appreciatedthat the methodologies disclosed hereinafter and throughout thisspecification are capable of being stored on an article of manufactureto facilitate transporting and transferring such methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer readabledevice or storage medium. In another aspect, the methodologies describedby FIGS. 7-9 and 13 can be facilitated by various embodiments describedherein, such as those associated with FIGS. 1-6 and 10-12.

With reference to FIG. 7, there is illustrated a methodology 700 fordetermining and/or identifying a source of a sample in accordance withvarious aspects of this disclosure. As an example, various mediaapplications, such as, but not limited to, mobile devices such as smartphones, tablets, PDA's, cooking utensils, and cookware can usemethodology 700. Specifically, methodology 700 receives a sample andidentifies the sample as associated with a source.

A mobile device can receive a sample and/or extrinsic data via a sampledelivery component at 702, (e.g., sample delivery component 112). Forexample, a fan can cause a sample to enter at least one aperture in ahousing of a mobile device. In another example, a sample can passivelyenter at least one aperture in a housing of a mobile device at 702. Inanother aspect, a user can wave or move a mobile device to manuallycause a sample to enter an aperture. As such, a mobile device cancontinuously monitor an airspace, such as to detect smoke or variouschemical compounds in an airspace. In various embodiments, the extrinsicdata can describe features associated with a sample or a source, captureconditions associated with a sample or source, user input, and the like.

At 704, a headspace of the sample can be analyzed by a detectioncomponent, for example. Analysis of a headspace can include a visual, achemical analysis, and/or particle analysis (e.g., via a sensory array).

At 706, a system can generate a query (e.g., via search component 1020described below) based on the analyzed headspace and/or extrinsic data.In an example, generating a query can comprise determining a set offilters to apply to the query. The filters can facilitate limitingpossible results to reduce a number of potential candidate matches.

At 708, a system can search (e.g., via search component 1020) a modelbased on the query. For example, the system can compare representationsof substances and/or compounds in the analyzed headspace with substancesand/or compounds associated with entries in a memory (e.g., a library ormodel). In another example, the system can filter a model based on thefilters represented in the query. Filtering the model can reduceavailable potential matches. For example, a query can instruct a searchengine to search of a plant that blooms during July in California. Thesearch engine can reduce potential candidate matches to plants meetingthe above criteria. In another example, once potential matches are foundbased on comparison of representations of headspaces, the system canfurther search matches based on pattern recognition of an image comprisein the query (e.g., compare an image with stored images).

At 710, the source associated with the headspace can be identified via adetection component, such as detection component 120. In an aspect, aresult of the search of a model can generate a set of candidate matches,confidence scores of the candidate matches, or other data associatedwith candidate matches. For example, a hash table analysis can result inone or more entries being associated with the headspace, background dataassociated with entries, and the like.

At 712, the identified source can be output as a result via an outputcomponent, such as output component 250. The output result can include aname of a source or sources, images of a source or sources, confidencescores, background information associated with the source(s), and/oradditional associated information. The additional associated informationcan include genes, definition, common location, and the like. It isnoted that the result can be received by a mobile device, rendered viaan interface, stored in a memory, and the like. In another aspect, aresult can be added to a model or library and/or utilized to alter amodel or library (e.g., train a model).

Turning now to FIG. 8, there is illustrated a methodology 800 fordetermining and/or identifying a source of a sample in accordance withvarious aspects of this disclosure. As an example, various mediaapplications, such as, but not limited to, mobile devices such as smartphones, tablets, PDA's, cooking utensils, and cookware can usemethodology 800. Specifically, methodology 800 receives a sample andidentifies the sample as associated with a source with use of additionalinput.

At 802, a sample is received via a sample delivery component (e.g.,sample delivery component 112). For example, a fan can cause a sample toenter at least one aperture in a housing of a mobile device. In anotherexample, a sample can passively enter at least one aperture in a housingof a mobile device at 802. As such, a mobile device can continuouslymonitor an airspace, such as to detect smoke or various chemicalcompounds in an airspace.

At 804, input is received and/or captured via one or more inputcomponent(s), such as display 610, a microphone 620, a housing 630,and/or a camera 650. Input can comprise multiple inputs such as but notlimited to user input, captured image, location information, dateinformation, and captured audio.

At 806, a headspace of the sample and input are analyzed by a detectioncomponent, such as detection component 120, for example. In one aspect,analysis of a headspace can include a visual analysis and/or a chemicalanalysis (e.g., via a sensory array). In another aspect, analysis ofinput can include audio analysis, text analysis, image analysis,location and date analysis, for example.

At 808, the analyzed headspace and the analyzed input are compared withentries in a memory, such as memory 114, for example. Comparison caninclude reducing possible sources to a set of possible sources viaanalyzed input, such as through a hash table, fuzzy logic and the like,via components executed by a CPU, such as CPU 130. In another aspect,the analyzed headspace can be compared to the reduced set of possiblesources.

At 812 a source or set of sources of the sample is determined, via adetection component, for example. In one aspect, the source or set ofsources can be associated with the analyzed sample and the analyzedinput. The association can be stored in memory, such as memory 114, forexample.

Turning now to FIG. 9, there is illustrated a methodology 900 fordetermining and/or identifying a source of a sample in accordance withvarious aspects of this disclosure. As an example, various mediaapplications, such as, but not limited to, mobile devices such as smartphones, tablets, PDA's, cooking utensils, and cookware can usemethodology 900. Specifically, methodology 900 receives a sample andidentifies the sample as associated with an entry in a data store.

At 902, a headspace of a received sample is analyzed, (e.g. by detectioncomponent 120). Analysis of a headspace can include a visual analysisand/or a chemical analysis (e.g., via a sensory array).

At 904, can analyzed headspace can be compared with entries in a server,such as server 360. In one aspect, the server can comprise a memory. Thememory can contain a set of entries. Each entry of the set of entriescan comprise a number of fields, such as a source name, source id, daterange, location, genera, genus, class, image and the like.

At 906, a source can be determined as associated with the analyzedheadspace. In one aspect, a set of source can be determined as possiblesources associated with the analyzed headspace.

Turning now to FIG. 9, there is illustrated a methodology 1300 fortraining a library or model in accordance with various aspects of thisdisclosure. A system can implement methodology 1300 can receive arepresentation of a headspace and extrinsic data. The system can updatea library based on the representation of the headspace and extrinsicdata. For example, a model (e.g., library) can be trained based ongathered data and the model can be utilized to generate results toqueries.

At 1302, a system can receive (e.g., via sample delivery component 312and/or an input component 326) a signature of a headspace and extrinsicdata. The signature can represent a determined composition of at least aportion of the headspace. In another aspect, the extrinsic data cancomprise user input, images, locations, dates, times, and the like.

At 1304, a system can search (e.g., via search component 1020) a librarybased on the signature of the headspace and the extrinsic data. Forexample, a system can generate filters based on extrinsic data and applythe filters and the signature of the headspace to generate a query. Thequery can be transmitted to a search engine (e.g., search component 1020or other search engine) that can search a model and identify a potentialmatch(es). In an aspect, the system can determine confidence scores ofthe matches.

At 1306, a system can alter (e.g., via inference component 1024described below) the library based on a result of the search, thesignature of the headspace, or the extrinsic data. For example, thesystem can alter a stored signature of a best match. An alteredsignature can be generated such that the received signature isencompassed by the best match. In another example, if no match is foundor no match exceeds a confidence score, then the signature can beidentified as a new entry. In another aspect, the extrinsic data can beadded to one or more entries to facilitate altering a library. As moreentries are added and/or entries are altered, a library can become morerobust and/or generate matches having higher confidence scores.

The systems and processes described below can be implemented withinhardware, such as a single integrated circuit (IC) chip, multiple ICs,an application specific integrated circuit (ASIC), or the like. Further,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodthat some of the process blocks can be executed in a variety of ordersthat are not all of which may be explicitly illustrated herein.

FIG. 10 illustrates an embodiment of a system 1000. System 1000 cancomprise a mobile device 1090 (e.g., personal digital assistants (PDAs),audio/video devices, mobile phones, MPEG-1 Audio Layer 3 (MP3) players,personal computers, laptops, tablets, etc.) that includes variousoptional components in connection with functionalities disclosed herein.The mobile device 1090 includes a system bus 1092 connected to aprocessor 1001 and memory 1016. The components can be electricallyand/or communicatively coupled to one another to perform variousfunctions. An intake component 1002 collects a sample (e.g., air, gas,vapor, . . . ) in connection with electronic olfactory-basedidentification thereof. The intake component can passively collectsamples or actively (e.g., employment of a fan, suction, MEMs device,negative pressure, or any other suitable means for collection a sample).A set of sensors 1004 sense properties associated with the sample orinputs to the device 1000. The sensors 1004 can optionally include anyone or more of the following: chemical sensor 1006, image sensor 1008,olfactory sensor 1011, vibration sensor 1012, or touch sensor 1014. Itis to be appreciated that other suitable sensors can be employed inconnection with device 1000.

Search component 1020 can be employed to allow a user to search forinformation, e.g., via the Internet, to augment identification of asample. In an aspect, the search results can be ordered as a function ofrelevancy to the search criteria, relevancy to user preferences, orrankings associated with the search results. The search component 1020can be implemented on a manual basis (e.g., user input), or in anautomated manner. For example, the search component 1020 can regularlyor constantly run searches (e.g., in the background to generate contentthat is relevant to a user at a current point in time).

An input component 1022 can receive information about a source (e.g., ofa smell). A detection component 1026 can detect presence and amount ofchemicals in the headspace, e.g., collected by the sensors 1004. Thesensors 1004 can react to various chemicals within a headspace. Thereaction can cause a change in physical or electrical properties ofrespective sensors. In one example, absorption of the chemicals in theheadspace causes physical alterations of various sensors in the set ofsensors. Each sensor can react differently to the various chemicals. Theprocessor 1001 can transform the reactions of the sensory array into adigital signal. For example, the digital signal can be computed based ona statistical model. In one non-limiting embodiment, an organicultra-thin transistor chemical sensor having a channel that consists ofone or a few monolayers can be employed. The organic thin filmtransistor chemical sensors can have nearly monolayer thin film channelsthat act as highly-sensitive detectors of trace levels of organic vaporsand can perform quantitative vapor analysis. The organic ultra-thin filmcan be permeable to a chemical analyte of interest.

An inference component 1024 can infer actions or conclusions inconnection with identification of a source or compound associated with agather sample. As used herein, the term “inference” refers generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. The inference component can perform autility-based analysis in connection with making an inference. Forexample, the cost of making an incorrect inference can be weighedagainst the benefit of making a correct inference.

Detection component 1026 can analyze chemical composition of a sample oranalyze a visual aspect of the sample. Pattern recognition component1028 can identify images captured by the device (e.g., via a cameracomponent). Audio recognition component 1030 can identify sources ofaudio received by the device. The device also includes a textrecognition component 1032. An analysis component 1034 analyzesinformation received from other components and can perform an analysisin connection with identifying source, smell, attribute, feature,composition, or the like associated with a sample.

Filtering component 1036 can employ and filter information to facilitatequickly converging on identification of source, smell, attribute,feature, composition, or the like in connection with a sample. Forexample, null items or features can be ruled out as potential candidatesin connection with determining identification. Input component 1038receives input, e.g., from a user. The input component 1038 can receivefor example, text, typed input, verbal or audio input, image input,gesture input, or any suitable type of input for inputting information.Output component 1040 outputs results of the analyses performed herein.Display component 1042 displays results of the analyses.

Various example embodiments are disclosed below to illustrate exemplaryapplications and/or functions of system 1000. It is appreciated that thevariations of the various embodiments can be utilized in otherapplications. As such, the exemplary embodiments are for illustrativepurposes and are not comprehensive of all envisioned embodiments.

In various embodiments, search component 1020 can generate queries fordetermining one or more features of a headspace or portion of aheadspace. The queries can be utilized by a local search engine orinference component 1024 to generate a result. In another example, thequery can be transmitted (e.g., via output component 1040) to a remotesearch engine and the remote search engine can generate a result basedon the query. It is noted that certain acts described in connection withdetection component 1026 can be performed by a detection componentremotely connected to mobile device 1090 (e.g., e.g., such as adetection component of a server device) or a search engine residing atleast in part on a remote device.

In an embodiment, search component 1020 can train a machine learningsystem to identify smells (e.g., sources of a sample). In an aspect, amodel can be trained using any number of samples that are analyzed byhumans. For example, users can provide input identifying a source of asample. For iterations of a user identifying a smell, the smell can bestored in one or more databases. The iterations can be utilized to traina model (e.g., generate a library of smells). The model can increase insize, robustness, and/or accuracy as user input increases. In an aspect,a number of identifiable smells (e.g., representations of samples matchwith a source) can continue to grow indefinitely.

In one or more embodiments, an initial model can be a pre-trained orseeded model that, once launched on a local device (e.g., mobile device110), can continue with its training on a per user/device basis. In anaspect, the model can be customized per owner. It is noted that acustomized (e.g., per user or group of users) model can be storedlocally or remotely (e.g., cloud based storage. In some embodiments, alocal model data (e.g., of mobile device 110) can be shared with aserver to enhance a server model/library. It is noted that a user canprovide input to opt-out of sharing a model.

In an embodiment, search component 1020 can utilize extrinsicinformation to reduce a number of possible results (e.g. sort knownscents). As described herein, extrinsic information can includevirtually any information associated with capturing a scent and/or userinput. For example, extrinsic information can comprise data describing acondition associated with capturing (e.g., receiving, gathering, etc.) asample, such as locations, GPS coordinates, seasons (e.g., season of ayear), time, date, user input (e.g., voice, text, etc.), images, and thelike. The extrinsic information can be utilized to

In embodiments, filtering component 1036 can generate filters based onthe extrinsic data. The filters can be utilized to generate a searchquery (e.g., via search component 1020) that is targeted to a particularspecies. In another example, the filters can facilitate generating aresult based on a user's desire. For example, a filter can comprise dataindicating the user desires to locate a retail store associated with ascent. In an example, a user can provide a voice command, textual query,or the like that indicates that the user desires to identify a scentbased on a species associated with a source. Search component 1020 canthen generate a query (e.g., to query a search engine) that facilitateslimiting a search to a set of candidate scents belonging to the desiredspecies or a set of possible results such as locations, suggestions,cures, and the like.

In another aspect, search component 1020 can further limit candidatescents based on additional extrinsic information. For example, filtercomponent 1036 can determine a current location (e.g., a restaurant),date, and time and search component 1020 can apply the current location,date, and time (e.g., as filters) to generate a query that furtherlimits potential candidate scents. In another example, a user canprovide a command “identify this food.” Filter component 1036 cangenerate a filter(s) based on the species, a current location, a time,and/or other extrinsic information. Selection component 1020 can applythe filter(s) to generate a query that is bound by the filter(s). Forexample, selection component 1020 can generate a query that is limitedto a food, a location, and a time. The query can be utilized by searchcomponent 1020 (and/or transmitted to a search engine) to generate aresult. In an example, the search engine can limit potential results torestaurants near the current location of mobile device 1090 and to foodavailable at the restaurants at the time. In one aspect, a currentlocation of the mobile device 1090 can be determined based on GPSlocation, a local access point, other devices, and the like. It is notedthat the above is but a limited set of examples; as such, searchcomponent 1020 can utilize virtually any criterion (extrinsic data) orcombination of criteria to limit candidate scents, improve accuracy,decrease processing time, or otherwise alter performance.

As another example, a user can identify a smell and system 1000 cancollect a headspace, perform an analysis (e.g., locally or through acloud-based analysis), and apply filters to facilitate properidentification of sources, scent signature matches, and generation of anidentified source. In some embodiments, system 1000 can generate orreceive a confidence score and provide the confidence score to a user(e.g., 99% confident that a scent includes garlic and onion). Aconfidence score can be determined based on matching a representation ofthe headspace and/or extrinsic data to a model. In one aspect, theconfidence score can be a weighted confidence score that assigns weightsbased on the representation of the headspace, extrinsic data, userprofiles, and the like.

In one or more embodiments, system 1000 can be configured to performspecialized functions or limited detection of substances. In an aspect,system 1000 can be pre-configured to identify a number of scents basedon constraints generated by filter component 1026. As noted above, theconstraints can be generated based on a detection mode or the like.

In one example, a user can trigger a particular mode or a mode can beentered automatically. For example, a user can trigger entering anallergy mode or an allergy mode can be entered based on detecting atrigger, such as a sneeze. In an aspect, an allergy mode can facilitatefilter component 1036 generating filters that facilitate, when appliedby search component 1020, limiting identifying a source associated witha headspace and providing a list of possible allergens. In accordancewith various embodiments disclosed herein, detection component 1026 canstore identified sources and compare the identified sources to determinea common possible allergen. In another example, filter component 1036can utilize extrinsic information (e.g., location, date, time, etc.) togenerate filters that limit results to possible allergens. In anotheraspect, search component 1020 can generate a query based on theextrinsic data and a remote search engine can generate a result. Theresult can be received by input component 1038 and displayed by displaycomponent 1042.

In various embodiments, inference component 1024/or a remote searchengine can determine results to search queries comprising a solution,warning, suggestion, or other descriptive data associated with a scentbased on identification of substances and/or extrinsic information. Forexample, a user can take a picture of source and/or utter a command toidentify the chemical makeup of the source (e.g., snaps a picture of ablade of a weed and utters identify this weed). Pattern recognitioncomponent 1028 can recognize an object (e.g., based on patternrecognition techniques) and audio recognition component 1030 canrecognize a command based on the user uttering the command. Thecombination of pattern recognition, audio recognition, and headspaceanalysis can facilitate filtering component 1036 generating one or morefilters to limit a search by search component 1020. In an aspect, searchcomponent 1020 can generate a query to search a model and a result canaccurately identify the type of plant as well as provide supplementalinformation regarding allergic properties, best ways of eradicate theweed, potential harmful or beneficial properties, or the like.

In another embodiment, a search engine and/or inference component 1024can provide a potential solution based on matched entries in a model.For example, radon gas, leaking oil or gas, or other chemicals, bodyodor, foot odor, type of bacteria associated with particular odors canbe detected and solutions to kill such bacteria can be generated. Inanother example, a result can comprise an alert. In an aspect, the alertcan be an audio notification, visual notification, vibration, or thelike. For example, if a pathogen is detected and the pathogen has a highconcentration (e.g., above a certain threshold) the mobile device 1090can generate an alert (e.g., via display component 1042) indicating thehigh concentration of the pathogen.

In another embodiment, inference component 1024 can determine adirection of a scent and can determine a navigational path to a likelysource of the scent. It is noted that inference component 1024 caninclude an accelerometer, gyroscope, GPS component, or the like. In someembodiments, intake component 1002 can receive or gather multiplesamples to determine a location or direction of a scent. For example, auser can walk in an environment and smell food, such as a barbequescent, which the user desires to locate. Sample delivery intakecomponent 1002 can gather samples as a user moves and detectioncomponent 1026 can determine whether a concentration of the scent isaltered (e.g., increase or decreased). In one aspect, intake component1002 can gather samples periodically (e.g., based on passage of time,distance traveled), randomly, semi-randomly, based on user commands, orthe like. As samples are gathered and/or analyzed, the concentrations ofa scent can be utilized to determine a direction for a user to travel inorder to locate the scent. For example, if a concentration or intensityis increasing, the user may be traveling in the proper direction. Inanother example, a user can smell some foul odor in a building or otherenvironment. The user can utilize mobile device 1090 to detect thelocation of the foul smell and remove the smell (e.g., spoiled food,rotten garbage, and the like).

In an implementation, mobile device 1000 can share models (e.g.,libraries) and/or utilize models in a cloud-computing environment. Forexample, mobile device 1000 can share (e.g., transmit) a local libraryto other mobile devices or servers. In an aspect, multiple libraries canbe aggregated and a robust library can be generated. In someembodiments, a library can be based in part on extrinsic data such alocation, date, time, images, and the like. For example, every timemobile device 1000 gathers a sample, location data (e.g., GPS location,date, time, etc.) can be recorded and/or attached to a model. Inferencecomponent 1024 can monitor location data can be matched with otherlocation data to provide a more detailed and/or improved model.

In an aspect, a library can be queried to determine candidate matchesbased on a representation of a headspace or extrinsic data. In one ormore embodiments, searching can comprise comparing entries in a libraryto determine a confidence score associated with entries of the library.The confidence score can be based on a level of match of representationsof headspaces and extrinsic data. In one example, entries can be storedas nodes of a hash table. Unique hashes or signatures can be assigned todifferent entries of the hash table. It is noted that the signatures canbe based on representations of headspaces. In another aspect, each entrycan be associated with extrinsic data (e.g., images, locations, dates,etc.). In other examples, signatures can be based on representations ofheadspaces in combination with extrinsic data. In another embodiment,inference component 1024 can updated and/or generate models based on amonitor history and/or results.

With reference to FIG. 11, a suitable environment 1100 for implementingvarious aspects of the claimed subject matter includes a computer 1102.The computer 1102 includes a processing unit 1104, a system memory 1106,a codec 1105, and a system bus 1108. The system bus 1108 couples systemcomponents including, but not limited to, the system memory 1106 to theprocessing unit 1104. The processing unit 1104 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1104.

The system bus 1108 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1106 includes volatile memory 1111 and non-volatilememory 1112. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1102, such as during start-up, is stored in non-volatile memory 1112. Byway of illustration, and not limitation, non-volatile memory 1112 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), or flash memory. Volatile memory 1111 includes random accessmemory (RAM), which acts as external cache memory. According to presentaspects, the volatile memory may store the write operation retry logic(not shown in FIG. 11) and the like. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), and enhanced SDRAM (ESDRAM).

Computer 1102 may also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 11 illustrates, forexample, a disk storage 1114. Disk storage 1114 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD)floppy disk drive, tape drive, Zip drive, LS-110 drive, flash memorycard, or memory stick. In addition, disk storage 1114 can includestorage media separately or in combination with other storage mediaincluding, but not limited to, an optical disk drive such as a compactdisk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1114 tothe system bus 1108, a removable or non-removable interface is typicallyused, such as interface 1116.

It is to be appreciated that FIG. 11 describes software, software inexecution, hardware, and/or software in combination with hardware thatacts as an intermediary between users and the basic computer resourcesdescribed in the suitable operating environment 1100. Such softwareincludes an operating system 1118. Operating system 1118, which can bestored on disk storage 1114, acts to control and allocate resources ofthe computer system 1102. Applications 1120 take advantage of themanagement of resources by operating system 1118 through program modules1124, and program data 1126, such as the boot/shutdown transaction tableand the like, stored either in system memory 1106 or on disk storage1114. It is to be appreciated that the claimed subject matter can beimplemented with various operating systems or combinations of operatingsystems. For example, applications 1120 and program data 1126 caninclude software implementing aspects of this disclosure.

A user enters commands or information into the computer 1102 throughinput device(s) 1128, non-limiting examples of which can include apointing device such as a mouse, trackball, stylus, touch pad, keyboard,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, electronic nose, web camera, andany other device that allows the user to interact with computer 11311.These and other input devices connect to the processing unit 1104through the system bus 1108 via interface port(s) 1130. Interfaceport(s) 1130 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1136 usesome of the same type of ports as input device(s) 1128. Thus, forexample, a USB port may be used to provide input to computer 1102, andto output information from computer 1102 to an output device 1136.Output adapter 1134 is provided to illustrate that there are some outputdevices 1136 like monitors, speakers, and printers, among other outputdevices 1136, which require special adapters. The output adapters 1134include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1136and the system bus 1108. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1138.

Computer 1102 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1138. The remote computer(s) 1138 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1102. For purposes of brevity, only a memory storage device 1140 isillustrated with remote computer(s) 1138. Remote computer(s) 1138 islogically connected to computer 1102 through a network interface 1142and then connected via communication connection(s) 1144. Networkinterface 1142 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), andcellular networks. LAN technologies include Fiber Distributed DataInterface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet,Token Ring and the like. WAN technologies include, but are not limitedto, point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1144 refers to the hardware/softwareemployed to connect the network interface 1142 to the bus 1108. Whilecommunication connection 1144 is shown for illustrative clarity insidecomputer 1102, it can also be external to computer 1102. Thehardware/software necessary for connection to the network interface 1142includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, wired and wireless Ethernetcards, hubs, and routers.

Referring now to FIG. 12, there is illustrated a schematic block diagramof a computing environment 1200 in accordance with this specification.The system 1200 includes one or more client(s) 1202, (e.g., computers,smart phones, tablets, cameras, PDA's). The client(s) 1202 can behardware and/or software (e.g., threads, processes, computing devices).The client(s) 1202 can house cookie(s) and/or associated contextualinformation by employing the specification, for example.

The system 1200 also includes one or more server(s) 1204. The server(s)1204 can also be hardware or hardware in combination with software(e.g., threads, processes, computing devices). The servers 1204 canhouse threads to perform transformations of media items by employingaspects of this disclosure, for example. One possible communicationbetween a client 1202 and a server 1204 can be in the form of a datapacket adapted to be transmitted between two or more computer processeswherein data packets may include coded analyzed headspaces and/or input.The data packet can include a cookie and/or associated contextualinformation, for example. The system 1200 includes a communicationframework 1206 (e.g., a global communication network such as theInternet) that can be employed to facilitate communications between theclient(s) 1202 and the server(s) 1204.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1202 are operatively connectedto one or more client data store(s) 1208 that can be employed to storeinformation local to the client(s) 1202 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1204 areoperatively connected to one or more server data store(s) 1211 that canbe employed to store information local to the servers 1204.

In one exemplary implementation, a client 1202 can transfer an encodedfile, (e.g., encoded media item), to server 1204. Server 1204 can storethe file, decode the file, or transmit the file to another client 1202.It is to be appreciated, that a client 1202 can also transferuncompressed file to a server 1204 and server 1204 can compress the fileand/or transform the file in accordance with this disclosure. Likewise,server 1204 can encode information and transmit the information viacommunication framework 1206 to one or more clients 1202.

The illustrated aspects of the disclosure may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Moreover, it is to be appreciated that various components describedherein (e.g., detection components, input components, sample deliverycomponents, and the like) can include electrical circuit(s) that caninclude components and circuitry elements of suitable value in order toimplement the aspects of this innovation(s). Furthermore, it can beappreciated that many of the various components can be implemented onone or more integrated circuit (IC) chips. In one exemplaryimplementation, a set of components can be implemented in a single ICchip. In other exemplary implementations, one or more of respectivecomponents are fabricated or implemented on separate IC chips.

What has been described above includes examples of the implementationsof the present invention. It is, of course, not possible to describeevery conceivable combination of components or methodologies forpurposes of describing the claimed subject matter, but it is to beappreciated that many further combinations and permutations of thisinnovation are possible. Accordingly, the claimed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims. Moreover,the above description of illustrated implementations of this disclosure,including what is described in the Abstract, is not intended to beexhaustive or to limit the disclosed implementations to the preciseforms disclosed. While specific implementations and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such implementationsand examples, as those skilled in the relevant art can recognize.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms used to describe such components are intended to correspond,unless otherwise indicated, to any component which performs thespecified function of the described component (e.g., a functionalequivalent), even though not structurally equivalent to the disclosedstructure, which performs the function in the herein illustratedexemplary aspects of the claimed subject matter. In this regard, it willalso be recognized that the innovation includes a system as well as acomputer-readable storage medium having computer-executable instructionsfor performing the acts and/or events of the various methods of theclaimed subject matter.

The aforementioned systems/circuits/modules have been described withrespect to interaction between several components/blocks. It can beappreciated that such systems/circuits and components/blocks can includethose components or specified sub-components, some of the specifiedcomponents or sub-components, and/or additional components, andaccording to various permutations and combinations of the foregoing.Sub-components can also be implemented as components communicativelycoupled to other components rather than included within parentcomponents (hierarchical). Additionally, it should be noted that one ormore components may be combined into a single component providingaggregate functionality or divided into several separate sub-components,and any one or more middle layers, such as a management layer, may beprovided to communicatively couple to such sub-components in order toprovide integrated functionality. Any components described herein mayalso interact with one or more other components not specificallydescribed herein but known by those of skill in the art.

Notwithstanding that the numerical ranges and parameters setting forththe broad scope of the invention are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspossible. Any numerical value, however, inherently contains certainerrors necessarily resulting from the standard deviation found in theirrespective testing measurements. Moreover, all ranges disclosed hereinare to be understood to encompass any and all sub-ranges subsumedtherein. For example, a range of “less than or equal to 11” can includeany and all sub-ranges between (and including) the minimum value of zeroand the maximum value of 11, that is, any and all sub-ranges having aminimum value of equal to or greater than zero and a maximum value ofequal to or less than 11, e.g., 1 to 5. In certain cases, the numericalvalues as stated for the parameter can take on negative values.

In addition, while a particular feature of this innovation may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. Furthermore, to the extent that the terms“includes,” “including,” “has,” “contains,” variants thereof, and othersimilar words are used in either the detailed description or the claims,these terms are intended to be inclusive in a manner similar to the term“comprising” as an open transition word without precluding anyadditional or other elements.

Reference throughout this specification to “one implementation,” or “animplementation,” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrase “in one implementation,” or “in an implementation,” in variousplaces throughout this specification are not necessarily all referringto the same implementation. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more implementations.

Further, references throughout this specification to an “item,” or“file,” means that a particular structure, feature or object describedin connection with the implementations are not necessarily referring tothe same object. Furthermore, a “file” or “item” can refer to an objectof various formats.

As used in this application, the terms “component,” “module,” “system,”or the like are generally intended to refer to a computer-relatedentity, either hardware (e.g., a circuit), a combination of hardware andsoftware, or an entity related to an operational machine with one ormore specific functionalities. For example, a component may be, but isnot limited to being, a process running on a processor (e.g., digitalsignal processor), a processor, an object, an executable, a thread ofexecution, a program, and/or a computer. By way of illustration, both anapplication running on a controller and the controller can be acomponent. One or more components may reside within a process and/orthread of execution and a component may be localized on one computerand/or distributed between two or more computers. While separatecomponents are depicted in various implementations, it is to beappreciated that the components may be represented in one or more commoncomponent. Further, design of the various implementations can includedifferent component placements, component selections, etc., to achievean optimal performance. Further, a “device” can come in the form ofspecially designed hardware; generalized hardware made specialized bythe execution of software thereon that enables the hardware to performspecific function (e.g., media item aggregation); software stored on acomputer readable medium; or a combination thereof.

Moreover, the words “example” or “exemplary” are used herein to meanserving as an example, instance, or illustration. Any aspect or designdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X employs A or B” isintended to mean any of the natural inclusive permutations. That is, ifX employs A; X employs B; or X employs both A and B, then “X employs Aor B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

What is claimed is:
 1. A mobile wireless communications devicecomprising: a wireless transceiver; a sample delivery component thatreceives a headspace sample associated with a scent; and a processorthat executes the following computer-executable components stored in amemory: a detection component that analyzes at least a portion of theheadspace sample and generates a representation of at least the portion;a filtering component that applies at least one filtering criterion tothe representation to facilitate identifying candidate matches to therepresentation; a search component that: generates a search query basedon at least the representation and the at least one filtering criterion;and wirelessly transmits the search query to a search engine thatperforms a search for the candidate matches; an input component thatwirelessly receives a result from the search engine, wherein the resultidentifies one or more candidate matches or features associated with thescent, wherein the search component generates the search query toidentify a product associated with the representation of at least theportion of headspace; and an audio recognition component or a textrecognition component receives instructions to purchase the identifiedproduct, and wherein the system effects purchase of the product.
 2. Thedevice of claim 1, further comprising a pattern recognition componentthat identifies a pattern in an image associate with the headspacesample, wherein the search component further generates the search querybased in part on the identified image.
 3. The device of claim 1, furthercomprising: an input component that receives a voice command, andwherein the search component further generates the search query based inpart on the voice command.
 4. The device of claim 1, further comprisingan inference component that trains a model to identify a source of atleast the portion based on a history of identifying portions ofheadspaces.
 5. A mobile wireless communications device comprising: awireless transceiver; a sample delivery component that receives aheadspace sample associated with a scent; and a processor that executesthe following computer-executable components stored in a memory: adetection component that analyzes at least a portion of the headspacesample and generates a representation of at least the portion; afiltering component that applies at least one filtering criterion to therepresentation to facilitate identifying candidate matches to therepresentation; a search component that: generates a search query basedon at least the representation and the at least one filtering criterion;and wirelessly transmits the search query to a search engine thatperforms a search for the candidate matches; and an input component thatwirelessly receives a result from the search engine, wherein the resultidentifies one or more candidate matches or features associated with thescent, wherein the search component generates the search query toidentify an animal associated with the representation of at least theportion of headspace.
 6. The device of claim 5, wherein: the sampledelivery component receives the headspace sample in an area thatcomprises an animal track; and the search component further generatesthe search query based at least in part on an image of the animal track.7. The device of claim 5, wherein the detection component determines alevel of freshness of the animal track based on a concentration of atleast the portion of headspace that is associated with the animal. 8.The device of claim 5, further comprising a navigation component thatdetermines, based on a concentration of the representation of at leastthe portion of headspace, a direction that, when traveled, leads to theanimal.
 9. The device of claim 8, wherein the navigation componentfurther determines a location of the animal based on the direction and aglobal positioning system.
 10. The device of claim 1, wherein thedetection component analyzes at least the portion of the headspacesample to detect a presence of an allergen, and the search componentgenerates the search query based on the allergen.
 11. The device ofclaim 1, further comprising: an output component that renders theresult, wherein the one or more features comprises at least one of asolution to eradicate a plant associated with the scent, a level ofdanger associated with the scent, or a possible cause of the scent. 12.A method, comprising: employing a processor, located in a mobile device,to execute computer executable components stored in a memory to performthe following acts: capturing a headspace of an airborne sample;analyzing the airborne sample to identify a composition of theheadspace; receiving extrinsic data describing an aspect of thecapturing; and querying, based on the composition of the airborne sampleand the extrinsic data, a library to determine a characteristic of asource of the airborne sample.
 13. The method of claim 12, wherein theextrinsic data describes at least one of a location of the capturing, animage associated with a potential source of the capturing, a date of thecapturing, or a desired function that limits the potential source. 14.The method of claim 12, wherein the acts further comprise: triggeringthe capturing of the headspace in response to detecting a triggeringevent, wherein the triggering event comprises at least one of a sneezeor a user exhaling.
 15. The method of claim 12, wherein the airbornesample is captured from an exhalation of the user's breath.
 16. Themethod of claim 15, wherein the acts further comprise: determining thecharacteristic as at least one of a level of pleasantness of the user'sbreath or a blood sugar level of the user.
 17. A mobile phone,comprising: a wireless transceiver; a sample delivery component thatgathers a headspace sample of an airborne scent; and a processor thatexecutes the following computer-executable components stored in amemory: a detection component that generates a representation of adetected composition of the headspace sample; a filtering component thatfilters the representation in connection with identifying potentialcandidate matches for the scent; a search component that queries asearch engine based on data describing the condition of the gatheringand the filtered representation of the detected composition, wherein thesearch engine performs a search to identify candidate matches for thescent; an input component that wirelessly receives a result from thesearch engine, wherein the result identifies one or more candidatematches or features associated with the scent, wherein the searchcomponent generates the search query to identify a product associatedwith the representation of at least the portion of headspace; and anaudio recognition component or a text recognition component receivesinstructions to purchase the identified product, and wherein the systemeffects purchase of the product.
 18. The mobile phone of claim 17,wherein the search engine searches a specialized model, based on expertopinions, to generate the result.
 19. The mobile phone of claim 17,further comprising a navigation component that determines, based on aconcentration of the representation, a direction that, when traveled,leads to a source of the headspace sample.
 20. The mobile phone of claim19, wherein the navigation component generates a marker identifying alocation associated with the gathering the headspace sample and at leastone of a date of the receiving the headspace sample, a time of thereceiving the headspace sample, or a determined concentration of thesource associated with at least the portion of the headspace sample.